Why you should always be sceptical of new funds promising miracle returns
Many market-beating strategies could be an illusion caused by the constant search for new ways to sell funds, says Cris Sholto Heaton.
There’s a well-known problem in many fields of research where findings from one group of researchers can’t be confirmed by others who try to repeat the same experiment. Areas such as psychology, sociology and medicine are especially prone to this – one study found that only half of a set of psychology experiments could be reproduced, with a number of high-profile and influential findings turning out to be false.
This isn’t necessarily because of fraud on the part of the original researchers; patterns will often emerge by pure chance if you crunch a set of data for long enough, and the incentive for researchers to keep crunching until this happens means that they can end up with a finding that appear to be statistically significant but is really just noise.
In theory, investing should have less of a problem than most fields. Ideas for strategies to deliver better performance will always be tested in the real world. If too many are failing, it should be obvious. Yet that assumption may be too optimistic, according to Campbell Harvey, professor of finance at Duke University in the US. In an interview with the Financial Times, he suggests that at least half of 400 market-beating strategies published in reputable financial journals can’t be replicated.
Results in the real world
Some researchers come to the same conclusion – a paper by Kewei Hou, Chen Xue and Lu Zhang (Replicating Anomalies) found that more than 80% of results they studied could not be reproduced. Others dispute it: Is There a Replication Crisis in Finance? by Theis Ingerslev Jensen, Bryan Kelly and Lasse Heje Pedersen reckons most findings they review appear to hold up. The debate remains live on an academic level, but there is some practical evidence to support it.
You’ve probably seen asset managers proudly launch new funds that have a great record in backtesting, but fail to deliver in the real world (this is true of many smart beta ETFs – see below). The chart above – from Research Affiliates and reproduced in a recent paper by Harvey – shows the average performance of the indices used for new US ETFs. On average, indexes strongly outperformed the market in the 36 months before ETF launches – but that outperformance tended to disappear over the next three years.
In some cases, the successful strategy will have been a statistical illusion from cherry picking or twisting data. Sometimes real-world transaction costs eat away at theoretical excess returns. Or the wider market environment may change (value strategies had a long record of success but have done badly for the past decade). Regardless of the reasons, investors should always be sceptical of new products promising miracle returns.
I wish I knew what smart beta was, but I’m too embarrassed to ask
Finance theory divides investment returns into two parts. Alpha is the value added through the decisions made by you (or the manager of the fund you hold). Beta is the return that results from the overall market.
Assume that a portfolio of investments goes up by 15% while the overall market rises 10%. In this case, beta is 10% and alpha is 5% (15%–10%). In reality, the calculation is a bit more complicated because it depends on whether the type of stocks in the portfolio would be expected to fluctuate more than the overall market, but this demonstrates the idea. Beta is what you get from simply being invested in the market (ie, what a passive index investor gets); alpha is what an investor gains or loses from active investment management.
Smart beta strategies lie between active and passive investing. A smart beta fund tracks an index, but with that index constructed differently to a traditional stockmarket index. Instead of weighting securities by size, a smart beta index selects or weights according to characteristics that may make them more likely to outperform the wider market. Common characteristics (often called factors) include variations on size (historically, small stocks tend to outperform larger ones on average); value (stocks that seemed cheap on metrics such as price/earnings or price/book have tended to outperform expensive ones); volatility (less volatile stocks have tended to do better); momentum (stocks that are rising strongly may be more likely to keep rising); and quality (profitable, efficient business with less debt have tended to beat weaker ones).
Advocates of smart beta say that it can deliver higher returns than passive investing in a cheaper and more systematic way than active investing. This makes sense in principle, but depends on the continued success of the factors chosen for the smart beta strategy. There can be no certainty that a factor that worked in the past will keep doing so.